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Record W2797856683 · doi:10.1093/jbcr/iry006.274

352 Prospective Observational Study Comparing Burn Surgeons’ Estimations and Photo-assisted Methods of Skin Graft Healing

2018· article· en· W2797856683 on OpenAlexaff
Kimberley S. Koetsier, Jason Wong, Lara A. Muffley, Gretchen J. Carrougher, T N Pham, Nicole S. Gibran

Bibliographic record

VenueJournal of Burn Care & Research · 2018
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineConcordanceInter-rater reliabilitySurgeryRating scaleInternal medicine

Abstract

fetched live from OpenAlex

Appropriate graft healing after split-thickness skin graft and early recognition of complications (infection, graft loss, shearing) are critical to burn patient management. Whereas prior retrospective studies have indicated good accuracy of providers’ bedside and photo assessments for graft ratios up to 1.5:1, it remains unclear whether larger graft ratios (up to 4:1), or alternative expansion techniques, such as Meek micrografting, influence the accuracy of graft healing assessments. This study evaluates the concordance of bedside and photograph assessments of graft healing among supervising clinicians at a regional burn center. We evaluated three assessment methods for graft epithelialization: 1) clinicians’ bedside rating, 2) clinician assessment of high definition photographs, and 3) Digital image analysis through color subtraction using Photoshop. We compared each method using a mixed-effects model on absolute agreement using intra-class correlation (ICC) and Bland-Altman (BA) plots. We prospectively enrolled 14 adult burn patients with 38 grafted wounds, to obtain 100 separate assessment sites. Bedside assessments had a mean ICC of 0.62 (compared to digital image analysis) and 0.70 (compared to photo assessment), with confidence intervals of +/- 30% healing on BA plots. Inter-rater reliability of photo assessment was excellent (0.94) among 4 clinicians for average measures. Repeated photo-assisted assessments had good to excellent intra-rater reliability (average ICC 0.88 for high definition photo assessment and 0.97 for digital analysis). Clinicians’ bedside assessments of graft epithelialization had high variability, whereas assessments by photographic techniques had excellent concordance. This study suggests that graft healing assessment can be performed reliably by using high-quality photographs. Clinicians’ judgment for graft epithelialization can be supplemented by use of high quality photographs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.356
GPT teacher head0.552
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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